Cleaning, micro-optimizations.

[master-thesis.git] / Parasitemia / Parasitemia / Classifier.fs

diff --git a/Parasitemia/Parasitemia/Classifier.fs b/Parasitemia/Parasitemia/Classifier.fs
index 7f11e13..0d21375 100644 (file)
--- a/Parasitemia/Parasitemia/Classifier.fs
+++ b/Parasitemia/Parasitemia/Classifier.fs
@@ -21,7 +21,7 @@ type private EllipseFlaggedKd (e: Ellipse) =
         member this.Y = this.Cy
 
 
-let findCells (ellipses: Ellipse list) (parasites: ParasitesMarker.Result) (img: Image<Gray, byte>) (config: Config.Config) : Cell list =
+let findCells (ellipses: Ellipse list) (parasites: ParasitesMarker.Result) (img: Image<Gray, float32>) (config: Config.Config) : Cell list =
     if ellipses.IsEmpty
     then
         []
@@ -93,31 +93,35 @@ let findCells (ellipses: Ellipse list) (parasites: ParasitesMarker.Result) (img:
         // We reverse the list to get the lower score ellipses first.
         let ellipsesWithNeigbors = ellipses |> List.map (fun e -> e, neighbors e) |> List.rev
 
-        // 2) Remove ellipses with a high standard deviation (high contrast).
+
+        // 2) Remove ellipses touching the edges.
+        for e in ellipses do
+            if e.isOutside w_f h_f then e.Removed <- true
+
+        // 3) Remove ellipses with a high standard deviation (high contrast).
 
         // CvInvoke.Normalize(img, img, 0.0, 255.0, CvEnum.NormType.MinMax) // Not needed.
 
         let globalStdDeviation = MathNet.Numerics.Statistics.Statistics.StandardDeviation(seq {
             for y in 0 .. h - 1 do
                 for x in 0 .. w - 1 do
-                    yield img.Data.[y, x, 0] |> float })
+                    yield float img.Data.[y, x, 0] })
 
         for e in ellipses do
-            let minX, minY, maxX, maxY = ellipseWindow e
-
-            let stdDeviation = MathNet.Numerics.Statistics.Statistics.StandardDeviation (seq {
-                for y in (if minY < 0 then 0 else minY) .. (if maxY >= h then h - 1 else maxY) do
-                    for x in (if minX < 0 then 0 else minX) .. (if maxX >= w then w - 1 else maxX) do
-                        if e.Contains (float x) (float y)
-                        then
-                            yield float img.Data.[y, x, 0] })
+            if not e.Removed
+            then
+                let shrinkedE = e.Scale 0.9
+                let minX, minY, maxX, maxY = ellipseWindow shrinkedE
 
-            if stdDeviation > globalStdDeviation * config.Parameters.standardDeviationMaxRatio then
-                e.Removed <- true
+                let stdDeviation = MathNet.Numerics.Statistics.Statistics.StandardDeviation (seq {
+                    for y in (if minY < 0 then 0 else minY) .. (if maxY >= h then h - 1 else maxY) do
+                        for x in (if minX < 0 then 0 else minX) .. (if maxX >= w then w - 1 else maxX) do
+                            if shrinkedE.Contains (float x) (float y)
+                            then
+                                yield float img.Data.[y, x, 0] })
 
-        // 3) Remove ellipses touching the edges.
-        for e in ellipses do
-            if e.isOutside w_f h_f then e.Removed <- true
+                if stdDeviation > globalStdDeviation * config.Parameters.standardDeviationMaxRatio then
+                    e.Removed <- true
 
         // 4) Remove ellipses with little area.
         let minArea = config.RBCMinArea